## y ymin ymax
## 1 0.4225771 0.3627116 0.4824426
## Parsed with column specification:
## cols(
## Species = col_character(),
## `Scientific Name` = col_character(),
## `Func. Group` = col_character(),
## Sightings = col_double(),
## Ingestions = col_double(),
## Removals = col_double(),
## Nibbles = col_double(),
## `Avg. Vistitation Rate` = col_double(),
## `Avg. Fruit Removal Rate` = col_double(),
## SDE = col_double(),
## Class = col_character()
## )
## [1] "Species" "Scientific Name"
## [3] "Func. Group" "Sightings"
## [5] "Ingestions" "Removals"
## [7] "Nibbles" "Avg. Vistitation Rate"
## [9] "Avg. Fruit Removal Rate" "SDE"
## [11] "Class"
## # A tibble: 2 x 4
## Height count mean sd
## <fct> <int> <dbl> <dbl>
## 1 high 25 0.301 0.387
## 2 low 45 0.0516 0.0580
## TH Tree Height visits fruit.rem.rate SDE
## 1 258_low 258 low 0.36210317 0.150000000 0.054315476
## 2 258_high 258 high 1.01686508 0.524450549 0.533295450
## 3 13_low 13 low 1.25000000 0.153005464 0.191256831
## 4 13_high 13 high 0.77380952 0.634146341 0.490708479
## 5 18_low 18 low 0.20568783 0.006410256 0.001318512
## 6 79_low 79 low 0.76315438 0.153846154 0.117408366
## 7 79_high 79 high 2.00983045 0.559900109 1.125304287
## 8 250_high 250 high 0.00000000 0.000000000 0.000000000
## 9 388_high 388 high 1.59523810 0.806991774 1.287344021
## 10 388_low 388 low 0.51785714 0.054347826 0.028144410
## 11 406_low 406 low 1.31944444 0.098039216 0.129357298
## 12 406_high 406 high 0.62500000 0.571428571 0.357142857
## 13 200_low 200 low 1.51515152 0.115942029 0.175669741
## 14 203_low 203 low 2.17532468 0.066798523 0.145308476
## 15 6_high 6 high 0.19439935 0.702077922 0.136483492
## 16 6_low 6 low 0.11842324 0.080536312 0.009537371
## 17 75_low 75 low 0.15625000 0.000000000 0.000000000
## 18 203_high 203 high 0.25595238 0.731884058 0.187327467
## 19 90_high 90 high 0.42981902 0.561310976 0.241262135
## 20 205_low 205 low 0.24122807 0.136363636 0.032894737
## 21 25_high 25 high 0.03472222 1.000000000 0.034722222
## 22 41_low 41 low 0.00000000 0.000000000 0.000000000
## 23 41_high 41 high 0.28645833 0.714285714 0.204613095
## 24 92_low 92 low 0.28905508 0.070530733 0.020387267
## 25 67_high 67 high 0.37500000 1.000000000 0.375000000
## 26 262_low 262 low 0.06410256 0.000000000 0.000000000
## 27 293_high 293 high 0.00000000 0.000000000 0.000000000
## 28 8_low 8 low 0.07575758 0.000000000 0.000000000
## 29 19_low 19 low 0.00000000 0.000000000 0.000000000
## 30 144_low 144 low 0.64406318 0.177388836 0.114249618
## 31 160_high 160 high 0.00000000 0.000000000 0.000000000
## 32 90_low 90 low 0.09572218 0.190476190 0.018232796
## 33 138_high 138 high 0.35054200 0.456794294 0.160125586
## 34 138_low 138 low 0.32778922 0.258405694 0.084702600
## 35 127_low 127 low 0.07502914 0.104761905 0.007860195
## 36 127_high 127 high 0.06944444 0.750000000 0.052083333
## 37 60_low 60 low 0.00000000 0.000000000 0.000000000
## 38 82_high 82 high 1.64368964 0.769627660 1.265029014
## 39 82_low 82 low 0.72448385 0.253885148 0.183935689
## 40 107_low 107 low 0.06578947 0.666666667 0.043859649
## 41 121_low 121 low 0.05341880 0.046875000 0.002504006
## 42 121_high 121 high 0.00000000 0.000000000 0.000000000
## 43 141_low 141 low 0.30102710 0.256465517 0.077203070
## 44 160_low 160 low 0.49533800 0.106162431 0.052586286
## 45 23_low 23 low 0.04941239 0.175000000 0.008647169
## 46 399_high 399 high 0.23103632 0.159663866 0.036888153
## 47 399_low 399 low 0.23698524 0.303921569 0.072024925
## 48 84_low 84 low 0.20834691 0.120035703 0.025009067
## 49 134_low 134 low 0.07470539 0.128205128 0.009577614
## 50 384_low 384 low 0.24621212 0.416666667 0.102588384
## 51 84_high 84 high 0.00000000 0.000000000 0.000000000
## 52 197_high 197 high 0.72916667 0.565217391 0.412137681
## 53 197_low 197 low 0.78125000 0.026666667 0.020833333
## 54 46_low 46 low 0.45454545 0.064814815 0.029461279
## 55 53_low 53 low 0.11363636 0.000000000 0.000000000
## 56 129_high 129 high 0.00000000 0.000000000 0.000000000
## 57 129_low 129 low 0.57849702 0.090425532 0.052310901
## 58 17_low 17 low 0.41666667 0.070422535 0.029342723
## 59 54_low 54 low 0.23674242 0.169706180 0.040176653
## 60 89_low 89 low 0.07352941 0.120000000 0.008823529
## 61 295_high 295 high 0.96590909 0.424640400 0.410164023
## 62 295_low 295 low 0.48413826 0.225877193 0.109355791
## 63 83_low 83 low 1.10294118 0.133858268 0.147637795
## 64 92_high 92 high 0.19943020 0.358333333 0.071462488
## 65 97_low 97 low 0.05208333 0.000000000 0.000000000
## 66 269_low 269 low 0.06410256 0.500000000 0.032051282
## 67 26_low 26 low 0.00000000 0.000000000 0.000000000
## 68 72_low 72 low 0.49242424 0.288888889 0.142255892
## 69 72_high 72 high 0.29166667 0.500000000 0.145833333
## 70 265_low 265 low 0.00000000 0.000000000 0.000000000
##
## Kruskal-Wallis rank sum test
##
## data: SDE by Height
## Kruskal-Wallis chi-squared = 8.7213, df = 1, p-value = 0.003145
## Df Sum Sq Mean Sq F value Pr(>F)
## Height 1 1.000 1.0005 18.15 6.43e-05 ***
## Residuals 68 3.748 0.0551
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Loading required package: carData
## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
##
## Attaching package: 'car'
## The following object is masked from 'package:boot':
##
## logit
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:purrr':
##
## some
## The following object is masked from 'package:dplyr':
##
## recode
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 19.494 3.705e-05 ***
## 68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Parsed with column specification:
## cols(
## Species = col_character(),
## `Scientific Name` = col_character(),
## `Func. Group` = col_character(),
## Sightings = col_double(),
## Ingestions = col_double(),
## Removals = col_double(),
## Nibbles = col_double(),
## `Avg. Vistitation Rate` = col_double(),
## `Avg. Fruit Removal Rate` = col_double(),
## SDE = col_double(),
## Class = col_character()
## )
## file saved to Table3.png
## file saved to SPPtable.pdf
## Note that HTML color may not be displayed on PDF properly.
## [1] "FD" "NFD"
## # A tibble: 2 x 4
## `Func. Group` count mean sd
## <fct> <int> <dbl> <dbl>
## 1 FD 7 2.14 2.05
## 2 NFD 13 0.446 0.454
## # A tibble: 20 x 11
## Species `Scientific Nam… `Func. Group` Sightings Ingestions Removals Nibbles
## <chr> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Centra… "Dasyprocta pun… NFD 260 0 44 70
## 2 Brown … "Metachirus nud… NFD 82 2 2 4
## 3 Baudó … "Penelope orton… FD 20 2 4 2
## 4 Choco … "Ramphastos bre… FD 272 99 142 3
## 5 Chestn… "Ramphastos amb… FD 316 157 168 1
## 6 South … "Nasua nasua " NFD 458 2 0 175
## 7 Collar… "Pecari tajacu" NFD 136 4 0 0
## 8 Kinkaj… "Potos flavus" NFD 84 0 3 14
## 9 Oilbird "Steatornis car… FD 73 0 36 0
## 10 Common… "Didelphis mars… NFD 100 1 0 27
## 11 Lowlan… "Cuniculus paca" NFD 416 0 31 73
## 12 Rufous… "Odontophorus e… NFD 434 0 0 4
## 13 Rodent… "" NFD 1380 0 197 45
## 14 Rufous… "Diplomys labil… NFD 5 0 0 1
## 15 Southe… "Amazona farino… FD 10 0 7 0
## 16 Squirr… "" NFD 675 0 249 109
## 17 Toucan… "" FD 284 75 85 0
## 18 Tome's… "Proechimys sem… NFD 136 0 24 1
## 19 Long-w… "Cephalopterus … FD 269 34 96 2
## 20 Brown … "Aramides wolfi" NFD 127 0 0 7
## # … with 4 more variables: `Avg. Vistitation Rate` <dbl>, `Avg. Fruit Removal
## # Rate` <dbl>, SDE <dbl>, Class <chr>
##
## One-way analysis of means (not assuming equal variances)
##
## data: SDE and Func_group
## F = 4.641, num df = 1.0000, denom df = 6.3171, p-value = 0.07238
##
## Kruskal-Wallis rank sum test
##
## data: SDE by Func_group
## Kruskal-Wallis chi-squared = 5.3009, df = 1, p-value = 0.02131
## Df Sum Sq Mean Sq F value Pr(>F)
## Func_group 1 13.05 13.054 8.462 0.00936 **
## Residuals 18 27.77 1.543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SDE ~ Func_group, data = spptable)
##
## $Func_group
## diff lwr upr p adj
## NFD-FD -1.693846 -2.917178 -0.4705144 0.0093621
## Df Sum Sq Mean Sq F value Pr(>F)
## Class 1 6.40 6.401 3.347 0.0839 .
## Residuals 18 34.42 1.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = SDE ~ Class, data = spptable)
##
## $Class
## diff lwr upr p adj
## Mammal-Bird -1.137172 -2.443006 0.1686627 0.0839294
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 6.2905 0.02194 *
## 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 5.2885 0.03365 *
## 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Tree Height Month Year Species utm1 utm2 ele Visitation
## 16 79 high 10 2016 Oenocarpus bataua 644127 38676 540 189
## Richness M.Richness B.Richness T.Ingestions T.Removal T.Nibble
## 16 5 0 5 57 117 0
## Avg.synch.neighbors Lek. DAP_CENSUS_1 ALTURA_CENSUS_1 NOTAS_CENSUS_1
## 16 NA LEK1 29.5 21 NA
## TIPO_DE_BOSQUE_COLLECTION DOSEL_CENSUS_1 CANOPY_DENS_CENSUS_1
## 16 Secundario 21 91.42
## ARB_DAP10_CENSUS_1 ARB_DAP50_CENSUS_1 CERCROPIA_CENSUS_1 MICONIA_CENSUS_1
## 16 9 0 0 0
## JUV_CENSUS_1 JUV_DENS_CENSUS_1 PLANTULA_CENSUS_1 PLANTULA_DENS_CENSUS_1
## 16 4 0.05092958 41 2.088113
## m.visitation b.visitation real.visitation real.m.visitation
## 16 0 189 189 0
## real.b.visitation real.richness real.m.richness real.b.richness date
## 16 189 5 0 5 42644
## focalmonth..50 focalmonth..100 focalmonth..150 focalmonth..200
## 16 2 3 6 9
## focalmonth..250 focalmonth..300 focalmonth..350 focalmonth..400
## 16 10 12 15 16
## focalmonth..450 focalmonth..500 TD FD NRFM NFM NRFG NFG start end
## 16 16 16 0 189 1 d 1 e 10/5/16 10/16/16
## days vis.rate n50 n100 n150 n200 n250 n300 n350 n400 n450 n500 Dates
## 16 11 17.18182 5 15 37 61 81 114 140 152 160 173 2016-10-01
## FD.rate TD.rate
## 16 17.18182 0
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0405307 | 0.1886986 | 0.2147905 | 0.8299306 |
| focalmonth..50 | 0.5152201 | 0.2956979 | 1.7423871 | 0.0814407 |
| Heightlow | 0.7238617 | 0.0437088 | 16.5610200 | 0.0000000 |
| focalmonth..50:Heightlow | -0.2752461 | 0.0374950 | -7.3408839 | 0.0000000 |
## real.visitation ~ focalmonth..450 * Height + offset(lograte) +
## (1 + focalmonth..450 | Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.2234468 | 0.3520308 | 0.6347365 | 0.5256003 |
| focalmonth..450 | -0.0047342 | 0.0302651 | -0.1564254 | 0.8756977 |
| Heightlow | 0.4050716 | 0.0852282 | 4.7527900 | 0.0000020 |
| focalmonth..450:Heightlow | 0.0042980 | 0.0059529 | 0.7220041 | 0.4702920 |
## Parsed with column specification:
## cols(
## X1 = col_character(),
## `z/tau value` = col_double(),
## `±SE` = col_double(),
## `p value` = col_double()
## )
## file saved to TableGLMM2.pdf
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0415496 | 0.1880433 | 0.2209575 | 0.8251255 |
| focalmonth..50 | 0.5152245 | 0.2960341 | 1.7404230 | 0.0817848 |
| Heightlow | 0.7235774 | 0.0436765 | 16.5667539 | 0.0000000 |
| focalmonth..50:Heightlow | -0.2750984 | 0.0374846 | -7.3389674 | 0.0000000 |
Results are qualitatively similar.
## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 |
## Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0439227 | 0.1998483 | 0.219780 | 0.8260425 |
| bin501 | 0.5713877 | 0.3322725 | 1.719636 | 0.0854987 |
| Heightlow | 0.6624992 | 0.0483892 | 13.691042 | 0.0000000 |
| bin501:Heightlow | -0.3084512 | 0.0692597 | -4.453547 | 0.0000084 |
Again, binarizing is qualitatively similar.
## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 |
## Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0276333 | 0.1891375 | 0.1461019 | 0.8838409 |
| bin50 | 0.5471455 | 0.2952029 | 1.8534560 | 0.0638170 |
| Heightlow | 0.7421483 | 0.0450024 | 16.4913175 | 0.0000000 |
| bin50:Heightlow | -0.3186904 | 0.0420803 | -7.5733801 | 0.0000000 |
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0650785 | 0.1908849 | 0.3409307 | 0.7331558 |
| focalmonth..50 | 0.3663191 | 0.3096886 | 1.1828626 | 0.2368636 |
| Heightlow | 0.6933769 | 0.0439534 | 15.7752735 | 0.0000000 |
| focalmonth..50:Heightlow | -0.0700859 | 0.0400875 | -1.7483212 | 0.0804084 |
Focalmonth..50 and the interaction term are no longer significant.
## real.visitation ~ focalmonth..50 * Height + offset(lograte) +
## (1 + focalmonth..50 | Tree)
| Estimate | Std. Error | z value | Pr(>|z|) | |
|---|---|---|---|---|
| (Intercept) | 0.0657147 | 0.1905568 | 0.3448563 | 0.7302024 |
| focalmonth..50 | 0.3664713 | 0.3096947 | 1.1833307 | 0.2366781 |
| Heightlow | 0.6931354 | 0.0439257 | 15.7797227 | 0.0000000 |
| focalmonth..50:Heightlow | -0.0699615 | 0.0400800 | -1.7455466 | 0.0808898 |
Results are qualitatively similar again when trees with 0 disperser visitation is removed
## file saved to euptcvm.pdf
## specialisation asymmetry H2
## 0.1895788 0.4342014
## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL
## 0.8235261 0.9700709
## generality.HL vulnerability.LL
## 12.9410832 3.5444669
## specialisation asymmetry H2
## 0.09440097 0.35740596
## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL
## 0.7975048 0.9144969
## generality.HL vulnerability.LL
## 6.0560145 3.7946460
##
## Shapiro-Wilk normality test
##
## data: dummy$fn1400
## W = 0.93961, p-value = 4.615e-06
##
## Shapiro-Wilk normality test
##
## data: dummy$FN50
## W = 0.69195, p-value < 2.2e-16
| statistic | p.value | kendall_score | denominator | var_kendall_score |
|---|---|---|---|---|
| 0.1360697 | 0.0378435 | 1118 | 8216.378 | 289349.8 |
| Visitation rate with random intercept | Visitation rate with random intercept AND SLOPE | |||||
|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.60 | 0.43 – 0.84 | 0.003 | 0.43 | 0.29 – 0.62 | <0.001 |
| FN50 | 0.97 | 0.88 – 1.07 | 0.548 | 1.79 | 1.31 – 2.45 | <0.001 |
| Height [low] | 0.71 | 0.62 – 0.81 | <0.001 | 0.80 | 0.70 – 0.92 | 0.001 |
| fn1400 | 1.04 | 1.02 – 1.05 | <0.001 | 1.04 | 1.03 – 1.06 | <0.001 |
| FN50 * Height [low] | 0.77 | 0.70 – 0.86 | <0.001 | 0.68 | 0.60 – 0.77 | <0.001 |
| Zero-Inflated Model | ||||||
| (Intercept) | 0.36 | 0.23 – 0.57 | <0.001 | 0.35 | 0.23 – 0.56 | <0.001 |
| FN50 | 0.87 | 0.59 – 1.27 | 0.467 | 0.89 | 0.60 – 1.32 | 0.561 |
| Random Effects | ||||||
| σ2 | 0.72 | 0.68 | ||||
| τ00 | 0.75 Tree | 0.94 Tree | ||||
| τ11 | 0.35 Tree.FN50 | |||||
| ρ01 | -0.84 Tree | |||||
| ICC | 0.51 | 0.54 | ||||
| N | 47 Tree | 47 Tree | ||||
| Observations | 151 | 151 | ||||
| Marginal R2 / Conditional R2 | 0.097 / 0.558 | 0.181 / 0.627 | ||||
| Visitation rate with random intercept | Visitation rate with random intercept AND SLOPE | Flying visitation rate with random intercept | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.60 | 0.43 – 0.84 | 0.003 | 0.43 | 0.29 – 0.62 | <0.001 | 0.34 | 0.16 – 0.73 | 0.005 |
| FN50 | 0.97 | 0.88 – 1.07 | 0.548 | 1.79 | 1.31 – 2.45 | <0.001 | 0.72 | 0.61 – 0.86 | <0.001 |
| Height [low] | 0.71 | 0.62 – 0.81 | <0.001 | 0.80 | 0.70 – 0.92 | 0.001 | 0.15 | 0.09 – 0.24 | <0.001 |
| fn1400 | 1.04 | 1.02 – 1.05 | <0.001 | 1.04 | 1.03 – 1.06 | <0.001 | 1.07 | 1.04 – 1.10 | <0.001 |
| FN50 * Height [low] | 0.77 | 0.70 – 0.86 | <0.001 | 0.68 | 0.60 – 0.77 | <0.001 | 0.12 | 0.03 – 0.42 | 0.001 |
| Zero-Inflated Model | |||||||||
| (Intercept) | 0.36 | 0.23 – 0.57 | <0.001 | 0.35 | 0.23 – 0.56 | <0.001 | 1.04 | 0.67 – 1.60 | 0.877 |
| FN50 | 0.87 | 0.59 – 1.27 | 0.467 | 0.89 | 0.60 – 1.32 | 0.561 | 0.80 | 0.44 – 1.44 | 0.452 |
| Random Effects | |||||||||
| σ2 | 0.72 | 0.68 | 1.71 | ||||||
| τ00 | 0.75 Tree | 0.94 Tree | 2.06 Tree | ||||||
| τ11 | 0.35 Tree.FN50 | ||||||||
| ρ01 | -0.84 Tree | ||||||||
| ICC | 0.51 | 0.54 | 0.55 | ||||||
| N | 47 Tree | 47 Tree | 47 Tree | ||||||
| Observations | 151 | 151 | 151 | ||||||
| Marginal R2 / Conditional R2 | 0.097 / 0.558 | 0.181 / 0.627 | 0.639 / 0.836 | ||||||
| Visitation rate with random intercept | Visitation rate with random intercept AND SLOPE | Flying visitation rate with random intercept | Non-Flying visitation rate with random intercept | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.60 | 0.43 – 0.84 | 0.003 | 0.43 | 0.29 – 0.62 | <0.001 | 0.34 | 0.16 – 0.73 | 0.005 | 0.46 | 0.25 – 0.85 | 0.013 |
| FN50 | 0.97 | 0.88 – 1.07 | 0.548 | 1.79 | 1.31 – 2.45 | <0.001 | 0.72 | 0.61 – 0.86 | <0.001 | 1.13 | 0.94 – 1.35 | 0.208 |
| Height [low] | 0.71 | 0.62 – 0.81 | <0.001 | 0.80 | 0.70 – 0.92 | 0.001 | 0.15 | 0.09 – 0.24 | <0.001 | 1.68 | 1.20 – 2.36 | 0.003 |
| fn1400 | 1.04 | 1.02 – 1.05 | <0.001 | 1.04 | 1.03 – 1.06 | <0.001 | 1.07 | 1.04 – 1.10 | <0.001 | 1.00 | 0.98 – 1.02 | 0.835 |
| FN50 * Height [low] | 0.77 | 0.70 – 0.86 | <0.001 | 0.68 | 0.60 – 0.77 | <0.001 | 0.12 | 0.03 – 0.42 | 0.001 | 0.79 | 0.66 – 0.95 | 0.013 |
| Zero-Inflated Model | ||||||||||||
| (Intercept) | 0.36 | 0.23 – 0.57 | <0.001 | 0.35 | 0.23 – 0.56 | <0.001 | 1.04 | 0.67 – 1.60 | 0.877 | 0.90 | 0.59 – 1.38 | 0.634 |
| FN50 | 0.87 | 0.59 – 1.27 | 0.467 | 0.89 | 0.60 – 1.32 | 0.561 | 0.80 | 0.44 – 1.44 | 0.452 | 0.81 | 0.58 – 1.15 | 0.240 |
| Random Effects | ||||||||||||
| σ2 | 0.72 | 0.68 | 1.71 | 0.51 | ||||||||
| τ00 | 0.75 Tree | 0.94 Tree | 2.06 Tree | 0.74 Tree | ||||||||
| τ11 | 0.35 Tree.FN50 | |||||||||||
| ρ01 | -0.84 Tree | |||||||||||
| ICC | 0.51 | 0.54 | 0.55 | 0.59 | ||||||||
| N | 47 Tree | 47 Tree | 47 Tree | 47 Tree | ||||||||
| Observations | 151 | 151 | 151 | 151 | ||||||||
| Marginal R2 / Conditional R2 | 0.097 / 0.558 | 0.181 / 0.627 | 0.639 / 0.836 | 0.036 / 0.605 | ||||||||